Population and housing grid spatialization in Yunnan Province based on grid sampling and application of rapid earthquake loss assessment:the Jinggu Ms6. 6 earthquake

Population and housing grid data spatialization hased on 340 grid samples （ 1 kmx 1 kin） is used in- stead of regional statistical data to simulate the population and housing distribution data of Yunnan Province （ 1 km×1 kin） for rapid loss assessment ibr the Jinggu Ms6.6 earthquake. The resuhs indicate that the method reflects the actual population and housing distribution and that the assessment results are eredihle. The method can be used to quickly provide spatial orientation disaster information after an earthquake.

A 100 m population grid in the CONUS by disaggregating census data with open-source Microsoft building footprints

In the Big Data era,Earth observation is becoming a complex process integrating physical and social sectors.This study presents an approach to generating a 100 m population grid in the Contiguous United States(CONUS)by disaggregating the US cen-sus records using 125 million of building footprints released by Microsoft in 2018.Land-use data from the OpenStreetMap(OSM),a crowdsourcing platform,was applied to trim original footprints by removing the non-residential buildings.After trimming,several metrics of building measurements such as building size and build-ing count in a census tract were used as weighting scenarios,with which a dasymetric model was applied to disaggregate the American Community Survey(ACS)5-year estimates(2013-2017)into a 100 m population grid product.The results confirm that the OSM trimming process removes non-residential buildings and thus provides a better representation of population distribution within complicated urban fabrics.The building size in the census tract is found in the optimal weighting scenario.The product is 2.5Gb in size containing 800 million populated grids and is currently hosted by ESRI(http://arcg.is/19S4qK)for visualization.The data can be accessed via https://doi.org/10.7910/DVN/DLGP7Y.With the accel-erated acquisition of high-resolution spatial data,the product could be easily updated for spatial and temporal continuity.

Modelling changing population distributions:an example of the Kenyan Coast,1979–2009

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates.Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes.Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit.Here we make use of recently released multi-temporal high-resolution global settlement layers,historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast.We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach.Strategies used to fill data gaps may vary according to the local context and the objective of the study.This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.

Exploring nationally and regionally defined models for large area population mapping

Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplines.Methods for disaggregating census data to finer-scale,gridded population density estimates continue to be refined as computational power increases and more detailed census,input,and validation datasets become available.However,the availability of spatially detailed census data still varies widely by country.In this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models.We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts.Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia.Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country.This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.

Limited Spatial Transferability of the Relationships Between Kriging Variance and Soil Sampling Spacing in Some Grasslands of Ireland:Implications for Sampling Design

Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems: i) different population vaxiograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.